1. Field of the Invention
The present invention relates generally to the field of machine fault diagnostics and, more particularly, to a system and method that uses predictive maintenance for on-line, real time monitoring of mechanical components for possible failures.
2. Related Art
Over the past few decades industry has taken a variety of steps to improve productivity and quality. However, little attention has been given to the area of maintenance. Maintenance in a broad definition is concerned with controlling the condition of equipment. Although maintenance exists in virtually every manufacturing company, it is often considered to be a support function of a manufacturing process. Only in recent years has maintenance been recognized as an integral part of the manufacturing process, able to increase productivity and quality.
With the increased use of robots, automation, and more sophisticated machines in manufacturing processes, it might be more appropriate to say that productivity and quality depend on machines rather than the person who operates the machine. Robots, for example, have replaced human operators in tasks, such as assembly, loading and unloading, spot welding, and inspection. Keeping this sophisticated equipment in a satisfactory condition increases both the amount and complexity of maintenance. Hence, more repair time and more highly trained, high-priced maintenance technicians and engineers are needed. This, of course, translates to higher maintenance costs.
When the degree of automation increases, maintenance cost also increases. In many companies, maintenance costs represent one of the larger parts of total operating costs - often more than direct labor cost. Therefore, a maintenance strategy that effectively reduces maintenance cost is important for a modern industry to remain competitive.
The three most common maintenance strategies are breakdown or corrective maintenance (i.e., fix the machine when it fails), preventive or time-based maintenance (i.e., maintain machine based on scheduled time), and predictive or condition-based maintenance (i.e., maintain machine before it fails).
For many years, most manufacturing companies used either breakdown or preventive maintenance. In such a case, the machinery is either allowed to breakdown or routine maintenance is performed to reduce the risk of machine failures. Nevertheless, breakdown maintenance is suitable only when a machine is not important, and is inexpensive to replace. If the cost of lost production, potential secondary damage to machinery, and potential safety risks are high, then this strategy is unacceptable. An apparent improvement to this strategy is to use preventive maintenance.
Although preventive maintenance can reduce the occurrence of machine breakdown, it also has some problems. First, the period between overhauls is very difficult to determine because machines and their components do not necessarily fail at regular intervals. Second, precious production time is lost because it is prudent to examine as many components as possible during the overhaul period. Third, parts in reasonable condition are often replaced unnecessarily.
Therefore, the best strategy appears to be to adopt a predictive maintenance strategy which predicts the condition, performance, and reliability of machinery, so that maintenance can be planned in advance. Recently, due to the increasing requirement of product quality and manufacturing automation, more and more manufacturing companies have adopted predictive maintenance as part of their maintenance program. They are doing so in order to increase reliability, productivity, and availability while minimizing costs of maintenance and overall plant operation.
Machine monitoring and diagnostics can be seen as a decision-support tool which is capable of identifying the cause of failure in a machine component or system, as well as predicting its occurrence from a symptom. Without accurate direction and identification of the machine fault, maintenance and production scheduling cannot be effectively planned and the necessary repair task cannot be carried out in time. Therefore, machine monitoring and diagnostics is essential for an effective predictive maintenance program.
The ultimate goal of using machine monitoring and diagnostics is to increase equipment availability, and in addition, reduce maintenance and unexpected machine breakdown costs. In order to maximize availability, one has to increase reliability by maximizing the mean time between failures and, at the same time, increase maintainability by minimizing the mean time to repair. As a result of constant monitoring and diagnostics, the frequency of unexpected machine breakdown is significantly reduced, and machine failure can be pinpointed immediately. As a result, reliability and maintainability are increased.
Machine monitoring and diagnostics can be done by simply listening to the sound generated during machine operation or visually examining the quality of machined parts to determine machine condition. In such a situation, however, the identification of a machine fault is totally dependent on the experience of the operator or engineer. Besides, many machine faults are not accurately assessed by relying only on visual or aural observations, especially during operation (e.g., wear and crack in bearings and gearboxes). Therefore, more sophisticated signal processing techniques, such as vibration analysis, oil analysis, acoustic emission, infrared, and ultrasound, have been developed to help the maintenance technician and engineer detect and diagnose machine failures.
The type of signal processing technique to be used for machine monitoring and diagnostics depends on the type of machine parameter to be monitored, as well as the type of fault to be tackled. There are a number of machine parameters which can be monitored, such as vibration, sound, temperature, force, pressure, motor current, lubricant oil, etc. Many studies have been conducted to determine which are most effective. Unfortunately, no parameter is able to indicate the full range of machine faults.
It is well-known that using a number of machine parameters in combination can produce a more accurate and reliable indication of machine condition. In such a case, maintenance personnel must be familiar with a number of different signal processing techniques, as well as their ability to detect certain types of faults. In addition, a large amount of data must be collected, analyzed, and understood. This means more time and knowledge are required for maintenance personnel to make a correct diagnosis.
Over the last two decades, most of the machine monitoring and diagnostic systems have been performed off-line using signal processing techniques. The success of these systems is not due to any one signal processing technique, but to the large amount of redundancy associated with multiple signal processing.
However, those signal processing techniques are very complicated to use; in addition, they must be performed by a highly trained and experienced human analyzer in order to make an accurate diagnosis. Accurate fault diagnostics is essential, especially in reducing product cycle time. As a result of correct and rapid fault diagnostics, equipment maintainability and availability can be improved significantly, thereby reducing the product cycle time. Although many new technologies, such as expert system, fuzzy sets, pattern recognition, and artificial neural networks, have been proposed to help achieve this goal, there is still no universal method available since each method has various capabilities and limitations.
Given below is a discussion of the four most prevalent techniques for machine monitoring and diagnostics--signal processing (e.g., vibration analysis and parametric modeling), artificial intelligence, artificial neural network, and sensor fusion.
Over the years, most machine monitoring and diagnostic systems have been performed by gathering the sensory data from the process, then analyzing the data off-line through a signal processing technique. One of the most widely used signal processing techniques is vibration analysis. This is because no other parameter can reveal as wide a range of machine fault types as vibration.
Vibration analysis deals with the extraction of information from measured vibration signals. It is well recognized that vibration characteristics will change as a machine condition changes. Wear or damage to rotating elements, imbalance, and resonance can generate excessive vibration.
Generally, vibration data can be analyzed in two different domains: time and frequency (J. Tranter, "The Fundamentals of, and The Application of Computer to, Condition Monitoring and Predictive Maintenance,", Proceedings of the 1st International Machinery Monitoring and Diagnostics Conference and Exhibit, Las Vegas, Nev., September 1989, pp. 394-401, and C. J. Li and S. M. Wu, "On-Line Detection of Localized Defects in Bearings by Pattern Recognition Analysis," Journal of Engineering of Industry, Vol. 111, November 1989, pp. 331-336). Time-domain analysis involves designing indices that are sensitive to the amount of impulsive vibrations observed. This technique includes overall level (RMS) measurements, peak level detection, crest factor, shock pulse, spike energy, kurtosis analysis, time waveform, and orbits. Frequency-domain analysis involves transforming the vibration waveform to show a train of impulses at different frequencies. This technique includes spectrum analysis, waterfall plot, cepstrum analysis, difference spectra, RMS of spectral difference, envelope analysis, high frequency resonance analysis (HFRT), and matched filter.
One of the most powerful vibration analysis techniques is spectrum analysis, which estimates the spectrum or power spectral density (PSD) from a vibration signal by performing a Fast Fourier Transform (FFT). The reason for the popularity of this FFT-based technique is because of its high computational speed. In addition, analysis of a machine vibration spectrum can yield important information regarding the condition of machine components because each rotating component in a machine generates identifiable frequencies; thus, changes at a given frequency range can be related directly to a specific component failure. However, there are some problems with this FFT-based technique, including low frequency resolution, implicit windowing of the data, and no significant data reduction.
In addition to spectrum analysis, the parametric modeling technique has been used for estimating the vibration spectrum. It is used in an attempt to alleviate the inherent limitations of the FFT approach mentioned above. Two major advantages of using a parametric modeling technique are: improvement of frequency resolution over FFT by suppressing the noise from the real signal, and reduction of data by using few parameters to describe the signal globally.
A number of parametric modeling techniques have been reported to estimate the vibration spectrum, for example, the autoregressive (AR) method, the autoregressive and moving average (ARMA) method, Prony's method, the minimum variance method, and the covariance method. A detailed review of these techniques can be found in (S. M. Kay and S. L. Marple, "Spectrum Analysis--A Modern Perspective," Proceedings of the IEEE, Vol. 69, No. 11, November 1981, pp. 1380-1419, and S. Braun, Mechanical Signature Analysis: Theory and Applications, Academic Press, London, 1986).
The parametric methods described above have been applied in the area of fault detection (see, e.g., Matsushima et al., "In-Process Detection of Took Breakage by Monitoring the Spindle Current of a Machine Tool," Proceedings of ASME Winter Annual Meeting, Phoenix, Ariz., 1982, pp. 145-154; M. Sidahmed, "Contribution of Parametric Signal Processing Techniques to Machinery Condition Monitoring," Proceedings of the 1st International Machinery Monitoring and Diagnostics Conference and Exhibit, Las Vegas, Nev., September 1989, pp. 190-195, S. Y. Liang and D. A. Dornfeld; "Tool Wear Detection Using Time Series Analysis of Acoustic Emission," Journal of Engineering for Industry, Vol. 111, August 1989, pp 199-205; Wu et al., "Signature Analysis for Mechanical Systems via Dynamic Data System (DDS) Monitoring Technique," Journal of Mechanical Design, Vol. 102, April 1980, pp. 217-221).
The disadvantage of parametric modeling is that it is not easy to find an optimal order for the model. The general guideline in the selection of the model order is based on the minimization of the sum of square errors. H. Akaike, "Power Spectrum Estimation through Autoregression Model Fitting,": Ann. Inst. Stat. Math., Vol. 21, 1969, pp. 407-419 and "A New Look at the Statistical Model Identification," IEEE Trans. Autom. Control, Vol. AC-19, December 1974, pp. 716-723, proposed two criteria, final prediction error (FPE) and Akaike information criterion (AIC), which can be used as the objective functions for order selection. In the recent work of C.C. Lin, "Classification of Autoregressive Spectral Estimated Signal Patterns Using an Adaptive Resonance Theory (ART)," Master's Thesis, Department of Industrial Engineering, The University of Iowa, Iowa City, August 1992, the order with the highest FPE and AIC level was selected as the optimal order.
Both parametric models mentioned above performed well in the early detection of machine failure. However, they are unable to identify the cause of the failure. This fault recognition task is usually done by the analyzer who identifies the cause of the fault by visual inspection of the spectrum. This is not an easy task because it requires experience and knowledge in order to make a correct diagnosis.
Although vibration analysis and parametric modeling techniques have been proven to be useful for machine monitoring and diagnostics, they are also knowledge-intensive techniques. In other words, they need to be performed by a highly trained and experienced engineer in order to identify the source of the machine fault correctly. To overcome this problem, an artificial intelligence approach has been proposed. In the past few years, the application of artificial intelligence to fault diagnostics has received much attention. Two of the most popular artificial intelligence approaches are expert systems and model-based reasoning.
One of the biggest successes in the field of artificial intelligence is expert systems. An expert system is a computer system which is programmed to exhibit expert knowledge in solving a particular domain problem. A typical expert system consists of the following components:
knowledge base (which contains knowledge about the problem, i.e., rules and facts), PA1 inference engine (which is the method for combining rules and facts PA1 explanation component (which explains why and how the conclusions are reached), PA1 user interface (which includes knowledge and data acquisition).
to reach conclusions),
Generally, the knowledge is represented in the form of an "if-then" rule. This rule is based on problem-solving heuristics generated by the expert. The inference engine controls the use of the knowledge base. Its control strategy can initiate from the facts or symptoms to reach a conclusion (forward chaining), or from a possible conclusion and search through the facts to verify the conclusion (backward chaining).
Many expert systems have been developed during the past several years for machine diagnostics. A detailed survey of fault diagnostic expert systems can be found in (S. G. Tzafestas, "System Fault Diagnosis Using the Knowledge-Based Methodology," Fault Diagnostics in Dynamic Systems: Theory and Applications, edited by R. Patton, P. Frank, and R. Clark, Prentice-Hall, New York, 1989).
Although expert systems are easy to use and able to provide expert knowledge to solve a specific domain problem, there are many difficulties in using this approach (J. M. David and J. P. Krivine, "Three Artificial Intelligence Issues in Fault Diagnosis: Declarative Programming, Expert Systems, and Model-Based Reasoning," Proceedings of the Second European Workshop on Fault Diagnostics, Reliability and Related Knowledge Based Approaches, UMIST, Manchester, Apr. 6-8, 1987, pp. 19-196), such as: difficulty in formalizing the problem, difficulty in obtaining knowledge, and difficulty in validating the system. In addition, there are many drawbacks with building an expert system for machine monitoring and diagnostics. One of the major drawbacks is its long execution time. This is particularly true when complex relations and a large knowledge base are involved in the reasoning process. Because expert systems must work through complex chains of reasoning in order to reach a conclusion, more processing time is required. Hence, the short response time required to perform on-line machine monitoring and diagnostics makes the application of expert systems in this area difficult and impractical.
As an alternate approach to expert systems, model-based reasoning has been proposed to solve diagnostic reasoning problems. One of the most promising techniques of model-based reasoning is "reasoning from structure and behavior" (R. Davis, "Diagnostic Reasoning Based on Structure and Behavior," Artificial Intelligence, Vol. 24, 1984, pp. 347-410). This technique begins with a description of the system, together with the observation(s) of the system behavior. If the observation conflicts with the way the system is meant to behave, then one concludes that a system failure has occurred. Given symptoms of misbehavior, possible fault candidates are identified using the structural model by following a dependency chain back from a violated prediction to each component that contributed to that prediction.
Many of the notable applications of model-based reasoning to diagnostic problems have been in the digital electronics field. This is because the structure of digital circuits can be represented in a fairly obvious way, and the intended behavior of the circuit is strongly implied by its structure.
Compared to the expert system approach, knowledge acquisition for the model-based system is easier. In addition, the model-based system is more robust and maintainable. It is able to diagnose multiple faults, avoiding exponential growth in the model size. However, it still poses problems for real-time diagnosis because the system has to look for all possible fault candidates and then has to classify them one by one according to likelihood, which means more reasoning time is needed.
The identification of a machine or component fault is actually a pattern recognition problem. In the past, a number of pattern recognition techniques, such as linear discriminant function and fuzzy sets, have been applied to solve this type of problem. Normally, these techniques classify machine or component condition into a two-state situation, i.e., normal or abnormal. Recently, artificial neural networks have been applied successfully in the area of machine monitoring and diagnostics. See for example, Dietz et al., "Jet and Rocket Engine Fault Diagnosis in Real Time," Journal of Neural Network Computing, 1989, pp. 5-18, Marko et al., "Automotive Control System Diagnostics Using Neural Nets for Rapid Pattern Classification of Large Data Sets,":, Processing of International Joint Conference on Neural Networks (ICJNN), Vol. II, 1989, pp. 13-15, Sunil et al., "Machining Condition Monitoring for Automation Using Neural Networks," Monitoring and Control for Manufacturing Processes: Presented at the Winter Annual Meeting of the ASME, Dallas, Tex., Nov. 25-30, 1990, pp. 85-100, Hoskins et al., "Incipient Fault Detection and Diagnosis Using Neural Networks," Proceedings of the International Joint Conference on Neural Networks (IJCNN), Vol. 1, 1990, pp. 81-86, T. I. Liu and E. J. Ko, "On-Line Recognition of Drill Wear via Artificial Neural Networks," Monitoring and Control for Manufacturing Processes: Presented at the Winter Annual Meeting of the ASME, Dallas, Tex., Nov. 25-30, 1990, pp. 101-110, Y. Guo and K. J. Dooley, "The Application of Neural Networks to a Diagnostic Problem in Quality Control," Monitoring and Control for Manufacturing Processes: Presented at the Winter Annual Meeting of the ASME, Dallas, Tex., Nov. 25-30, 1990, pp. 111-119, T. I. Liu and J. M. Mengel, "Detection of Ball Bearing Conditions by an A.I. Approach," Proceedings of the Winter annual Meeting of the ASME, Atlanta, Ga, Dec. 1-6, 1991, pp. 13-21, and G. M. Knapp and H. P. Wang, "Machine Fault Classification: A Neural Network Approach," International Journal of Production Research, Vol. 30, No. 4, 1992, pp. 811-823.
One of the greatest problems with artificial neural networks is that neural networks never explain themselves. In order to eliminate this so-called "black box" approach to neural network applications, it is necessary to build an explanation capability into a neural network system. An apparent approach is to combine expert systems and neural networks into a hybrid system. Examples of combining expert systems and neural networks can be found in M. Caudill, "Using Neural Nets: Hybrid Expert Networks," AI Expert, November 1990, pp. 49-54, D. V. Hillman, "Integrating Neural Nets and Expert Systems," AI Expert, June 1990, pp. 54-59, Kraft et al., "Hybrid Neural Net and Rule Based System for Boiler Monitoring and Diagnosis," Proceedings of the 53rd Annual Meeting of the American Power Conference, Chicago, Ill., Apr. 29-May 1, 1991, pp. 952-957 and Rabelo et at., "Synergy of Artificial Neural Networks and Knowledge-Based Expert Systems for Intelligent FMS Scheduling," Proceedings of the International Joint Conference on Neural Networks (IJCNN), Vol. 1, 1990, pp. 359-366.
Sensor fusion, sometimes referred to as multisensor integration, is a process of integrating the information obtained from a variety of sensors. It is utilized with the hope of achieving human-like performance (i.e., the ability to effectively combine information from his or her senses) in decision-making, especially in applications of image or signal processing where the information from individual sensors are generally noisy, uncertain, and insufficient.
There are four key advantages of using sensor fusion. First, fusion of redundant information obtained from a group of sensors (or a single sensor over time) concerning the same feature can increase accuracy as well as enhance reliability in the case of sensor error or failure. Second, the complementary information can be yielded by using multiple sensors to measure different aspects of the feature if the required information could not be obtained by individual sensors acting alone. Third, multiple sensors can provide more timely information, as compared to the speed at which it could be provided by a single sensor, particularly when parallelism is involved in the integration process. Fourth, multiple sensors can provide required information at a lower cost when compared to the equivalent information obtained from individual sensors. (See J. M. Fildes, "Sensor Fusion for Manufacturing," Sensors, January 1992, pp. 11-15, and R. C. Luo and M. G. Kay, "Multisensor Integration and Fusion: Issues and Approaches," Sensor Fusion: Proceedings of the SPIE, Vol. 931, 1988, pp. 42-49).
The objective of sensor fusion is to combine individual information into a representative pattern that provides a higher level of information than the sum of the information from individual sensors. The information from individual sensors can be raw data or processed data. The processed data is normally generated by a preprocessing procedure which performs pattern recognition, noise filtering, or data reduction. It can be in the form of either estimates of parameters (such as parameters of autoregressive model), or evidence supporting certain propositions, or decisions favoring certain hypotheses.
Determining a method to integrate different types of sensors in order to provide reliable and consistent information is the most challenging task in sensor fusion. However, a large number of methods are available to achieve this task. These methods extend from low-level probability distributions for statistical inference to high-level production rules for logical inference. See R. C. Luo and M. G. Kay, "Multisensor Integration and Fusion: Issues and Approaches," Sensor Fusion: Proceedings of the SPIE, Vol. 931, 1988, pp. 42-49, for a review of six general methods for sensor fusion. Additionally, G. Chryssolouris and M. Domroese, "Sensor Integration for Tool Wear Estimation in Machining," Sensors and Controls for Manufacturing: presented at the Winter Annual Meeting of the ASME, Chicago, Ill., Nov. 27-Dec. 2, 1988, pp. 115-123, and "An Experimental Study of Strategies for Integrating Sensor Information in Machining," Annals of the CIRP, Vol. 38, No. 1, 1989, pp. 425-428, provide a review and comparison of four different methods for sensor fusion, and conclude that a neural network approach is more effective in learning a relationship for providing parameter estimates, particularly when the relationship between the sensor-based information and the actual parameter is nonlinear; and a neural network approach is less sensitive to deterministic errors in the sensor-based information than the other three approaches.
Several popular approaches in the area of machine monitoring and diagnostics have been discussed above. Each approach has its strengths and weaknesses. A significant amount of research has been conducted in the development and application of each individual approach. However, little has been done in incorporating these different approaches into an intelligent system.